Abstract
Wireless Body Area Networks (WBAN), in particular in the field of wearable health monitoring system (WMB), such as electromagnetic cardiograms (ECG) data collecting system via WBANs in e-health applications, is becoming increasingly important for future communication systems. Compressive sensing (CS), on the other hand, has been shown to consume less power compared classic transform-coding-based approaches. We propose a new low-rank sparse deep signal recovery algorithm for recovering ECG data in the context of CS (Compressive sensing) because the spatial and temporal data collected by a WBAN have some closely correlated structures in certain wavelet domains e.g., the discrete wavelet transform (DWT) domain
Highlights
For statistical inference in wireless connections, distributed signal processing techniques are used
A WLAN network allows a WLAN hotspot to be extended along a greater geographical region without any need for cables to be attached to each access point (AP)
The MSE was evaluated between -30 to -40 db and it seems to be increasing
Summary
For statistical inference in wireless connections, distributed signal processing techniques are used. Data is extracted from data gathered at nodes dispersed along a geographic region. A group of surrounding nodes collects nearby data for each node, together including their local estimations for a better estimate, and sends it to each node [1][2]. Distributed signal processing (DSP) is a networking design and evaluation approach that may solve optimization and adaptation issues in a distributed and productive way [3]
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